Embracing the Power of Analytics for Digital Transformation

‘Going Digital’ is no longer a question of ‘why’ but ‘when’! Every technology - be it Mobile applications, Wearables, Web portals, IoT (Internet of Things), Blockchain, or APIs – is powered by analytics which drives business value and growth. The true workhorse behind digital transformation is the presence of multi-dimensional data that can provide powerful business insights when it is processed by intelligent and relevant analytics tools.

Digital transformation has turned every company - from manufacturing to retail – into a technology company at its core. Nike, Nestle, Disney, Uber are just a few popular examples. While the century-old manufacturing company like GE has reinvented itself with a proprietary data analytics platform, new-age companies like Uber have stormed into the world by harvesting agile and smart analytics to create a level-playing field and unbeatable value for the customer.

Hyper-personalization is just one such business value that can elevate great customer experience into real customer delight. This provides a market opening that can set off a virtuous cycle of business growth through increase in repeat purchases, customer loyalty, spontaneous reviews on social media and enthusiastic referrals to customer networks, for example. Advanced analytics embedded in every customer interaction and business process is driving the next wave of workforce productivity and business growth.

On an Evolutionary Path

The classification of Data Analytics has been part of an evolutionary journey. Broadly, it can be classified into 4 types:

Descriptive analytics: This is the most preliminary form of analysis that organizes historical data and prepares the basic summary of what has happened in the past or is happening right now for further processing.

Diagnostic analytics: This drills-down into the organized data to mine/discover real root causes behind the actual results to show why it is happening.

Predictive analytics: This form of analytics uses techniques from modeling to machine learning to make informed predictions of future events.

Prescriptive analytics: This focuses on helping a business with finding the best course of action by combining descriptive and predictive analytics.

Need for Real Data and other Prerequisites

Data Diversity: As data volume grows exponentially, the diversity of its sources is growing as well. There is relevant data tucked away in primary sources such as IoT sensors, log files, social media, audio/video files, call center logs and other internal data such as emails, chats and repositories. Organizations which are able to marshal and harness this prolific data volume are likely to survive.

Data Volume: There is data everywhere and a lot of it. It is imperative that you acquire, store and organize all the internal data as well as the external unstructured/semi-structured data from call logs, audio/video files, customer surveys from the organization. Any application of analytics on only a cross-section of the data will be far less effective.

Data Purpose: While big data analytics is the watchword these days, it will not yield much value unless you narrow your analytics solution to a specific business or a use case. Otherwise, it can be a challenge to decipher the patterns or examples that are revealed by the abundance of data. Just having vast amount of data for analysis doesn’t mean you can extract the insight you need; use cases are crucial.

Applications of Analytics

There are a number of analytics algorithms that are tailored for specific applications with the above prerequisites in mind. They provide significant business value through predictive maintenance, optimized supply chain, and smart self-replenishing equipment which can order supplies or spares automatically, for example. They can be categorized as the following 4 fundamental applications:

Mobile Analytics: The mobile-first strategy of several businesses has given rise to a lot of data. Useful customer data gets generated with ubiquitous mobile technology as customers are finding nearby restaurants, paying bills or navigating through the city. Mobile analytics can help organizations understand their customer behavior better and increase opportunities for monetization of apps and overall revenue.

Real-Time Analytics: It helps to get the right products in front of the people looking for them or offering the right promotions to the people most likely to buy. For gaming companies, it helps in understanding gamers and crafting an individualized approach to reach them. Targeted promotions are much more effective than large scale broad-spectrum promotions.

IoT Analytics: IoT sensors can transmit data at millisecond intervals and with the help of cloud, this large collection of data can be readily stored. But, just storing this data is an inert exercise which will not add any business value, unless it is also analyzed for actionable insights.

Key Use Cases

Here is how NIIT Technologies has helped its customers on big data analytics:

Data is the new oil, but it is crude, and cannot really be used unless it is refined with analytics to bring the new gold nuggets. Proactive decision making is made possible by forecasting, optimization, text mining and predictive analytics. When that is combined with the capability to deliver an empathetic customer experience through hyper-personalization, it can start turning your business levers for revenue growth and customer retention. To learn about data and analytics offerings by NIIT Technologies
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